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Towards robust trajectory similarity computation: Representation-based spatio-temporal similarity quantification

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Abstract

Quantifying the trajectory similarity is a fundamental functionality in analysis tasks of spatio-temporal data. Existing classic methods compute the trajectory similarity based on point matching, which are unable to cope with low-quality trajectories (e.g., have non-uniform sampling rates or noise points), especially when we take both spatial coordinates and the time components into account. While some studies with deep learning methods exist, they did not consider the time components of trajectories and the robustness of similarity measure simultaneously, thus they fail to retrieve similarity-based queries in spatio-temporal databases where time components of trajectories are also important. In practice, the time-aware trajectory similarity computation can be better applied to diverse scenarios, yet the time complexity also heavily increases. To enable efficient and robust similarity computation on massive-scale trajectories, we developed a novel RSTS model based on deep representation learning, in which we take the time components into account. Extensive experiments show that our proposal constantly outperforms another two methods, and the similarity measure based on our RSTS model is robust to low-quality trajectories.

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Data availability

All datasets used in this paper are open datasets.

Notes

  1. For simplicity, hereon we will use token and cell interchangeably to refer to a spatio-temporal cell where the context is clear.

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    Space partition and token generation

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  2. https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/

  3. https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i/data

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Acknowledgements

We also acknowledge the editorial committee’s support and all anonymous reviewers for their insightful comments and suggestions, which improved the content and presentation of this manuscript.

Funding

This work was supported by the NSFC (U2001212, U21B2046, 62032001, and 61932004).

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  1. University of Electronic Science and Technology of China, Xiyuan Avenue, 611731, Chengdu, China

    Ziwen Chen, Ke Li, Silin Zhou, Lisi Chen & Shuo Shang

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Contributions

All authors contributed to the study conception and model design. Ziwen Chen and Ke Li worked on the full manuscript. The first draft of the manuscript was written by Ziwen Chen and Ke Li. Lisi Chen and Shuo Shang wrote the Section 1–2. Ziwen Chen and Ke Li prepared the Section 3–5. The expermental study was conducted by Silin Zhou. All authors commented on previous versions of the manuscript. All authors proof-read and approved the final manuscript.

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Chen, Z., Li, K., Zhou, S. et al. Towards robust trajectory similarity computation: Representation-based spatio-temporal similarity quantification. World Wide Web 26, 1271–1294 (2023). https://doi.org/10.1007/s11280-022-01085-4

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